Recent metric learning approaches usually leverage more complex architectures to improve performance, e.g. adding several FC layers after the flattened feature or global pooling. Although different metric learning applications have different head architectures (e.g. GAP+FC) on CNN, the basic components are highly similar.
Given the introduction of activation decomposition on a simple architecture in the previous section, we present a unified extension to make our method applicable to most existing state-of-the-art CNN architectures for various applications, including image retrieval [1]}, face recognition [2]}, person re-identification [3]} and image geo-localization [4]}. Specifically, in Section REF , we address linear components by considering them together as one linear transformation. Then Section REF  focuses on transforming nonlinear components to linear ones in the inference phase.
